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A mountain of big data, a mountain of storiesBy
SAN FRANCISCO – Big Data tends to be regarded as a treasure chest crammed with latent sales, which can lure brands away from the benefits of traditional storytelling, according to panelists at a session March 26 at ad:tech San Francisco 2014.
During “The Myth of Big Data” session, speakers sought to clarify the foggy reverence that often surrounds the concept. Rather than viewing traditional storytelling and Big Data as divided methods that achieve the same result, the session emphasized that the two are much stronger together than apart.
“Anyone who is sitting on a mountain of Big Data, is also sitting on a mountain of stories,” said Shane Snow, cofounder and chief creative officer of Contently, New York.
“At the same time that you’re personalizing and sending people to your shopping carts, and using data to make mcommerce more efficient, you can also use the exhaust from that data to bring the brand closer to customers and build trust,” he said.
“The numbers are cold, but as marketers we need to connect with people on a human level.”
Lifting the fog
Big Data is often touted as the antidote to brand ailments. If the mysteries hidden within big data could only be obtained, then consumer engagement and sales will rise.
However, the reality is more complex.
Dan Greenberg, cofounder/CEO of Sharethrough, sees Big Data as a “mirror that forces you to be intellectually honest.”
Brands that might otherwise be complacent with the outcome of an ad or campaign can now be forced to make adjustments or start from scratch when confronted with the unflinching facts of Big Data.
For instance, a brand may fawn over a campaign video with a slow build-up so much so that they fail to tailor it for a YouTube Trueview ad, an ad that can be skipped after five seconds. In this situation, Big Data would act as the enforcer that nobody else wants to be by stating that a video with a slow-build up would get skipped almost every single time.
On the other hand, Big Data can act as a confinement.
Terry McDonell, former of editor of Sports Illustrated, discussed how consumer surveys intended to shape the makeup of the magazine according to reader interests always yielded the same results, yoking the staff to content that was predictable and failed to generate new subscriptions.
The editor stressed that he was able to boost subscriptions only when he lessened the magazine’s insistence on data points and ramped up in-depth storytelling.
Joe McCambley, founder and creative director of The Wonderfactory, said that Big Data has three primary goals: to determine “what have people done in the past, what do people or things need right now and what might people or things do in the future.”
Obviously, big data addresses the first goal. The second goal can be a fickle proposition, since what is trending quickly changes. Predicting what will occur in the future presents the most problems.
The panelists argued that testing permutations of a campaign such as headlines or thumbnail images within the first 24 hours of its release will allow brands to make adjustments that improve content for the rest of its scheduled duration.
Ultimately, predicting the results of a campaign beforehand is a riddle yet to be solved.
“After two or three years of working on this, the best we can get is that in October people will be interested in pumpkin recipes,” Contently’s Mr. Snow said.
Joe McCarthy, editorial assistant on Luxury Daily, New York
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